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The buildup of plaques in atherosclerosis leads to cardiovascular events, with chronic unresolved inflammation and overproduction of reactive oxygen species (ROS) being major drivers of plaque progression. Nanotherapeutics that can resolve inflammation and scavenge ROS have the potential to treat atherosclerosis. Here we demonstrate the potential of black phosphorus nanosheets (BPNSs) as a therapeutic agent for the treatment of atherosclerosis. BPNSs can effectively scavenge a broad spectrum of ROS and suppress atherosclerosis-associated pro-inflammatory cytokine production in lesional macrophages. We also demonstrate ROS-responsive, targeted-peptide-modified BPNS-based carriers for the delivery of resolvin D1 (an inflammation-resolving lipid mediator) to lesional macrophages, which further boosts the anti-atherosclerotic efficacy. The targeted nanotherapeutics not only reduce plaque areas but also substantially improve plaque stability in high-fat-diet-fed apolipoprotein E-deficient mice. This study presents a therapeutic strategy against atherosclerosis, and highlights the potential of BPNS-based therapeutics to treat other inflammatory diseases.

Ring laser gyroscope (RLG) is one of the most important equipment in high-precision strap-down inertial navigation systems and has been widely used in the field of navigation. At present, there is little research on the application of RLG on the angle error measurement of sub-arc-second in the metrology field, and it has not been officially used as the metrology standard instrument. Aiming at the inaccurate measurement of RLG in metrology due to zero position drift and noncoplanar installation, a noncontact high-precision angle measurement error measurement method is proposed in this article. First, the original scale factor is calculated on the standard rotating device to avoid re-calculated on the measured device and solve the traceability problem. Second, the influence of the installation error between the turntable and the laser gyroscope goniometer on the angle measurement error is analyzed, and the compensation method for angle measurement error in the online measurement of the laser gyroscope is proposed. Finally, the feasibility of online metrology of RLG is verified by experiments, and the measurement uncertainty is estimated. The online measurement results show that compared with the photoelectric autocollimator (measurement uncertainty is $U = 0.07^{\prime \prime }({k = 2})$ , the angle measurement error of the RLG goniometer (RLGG) in multiaxis turntable is $ \pm 0.4^{\prime \prime }$ , and the measurement uncertainty is $U = 0.36^{\prime \prime }({k = 2})$ .

Endowing quadruped robots with the skill to forward jump is conducive to making it overcome barriers and pass through complex terrains. In this paper, a model-free control architecture with target-guided policy optimization and deep reinforcement learning (DRL) for quadruped robot jumping is presented. First, the jumping phase is divided into take-off and flight-landing phases, and optimal strategies with solt actor-critic (SAC) are constructed for the two phases respectively. Second, policy learning including expectations, penalties in the overall jumping process, and extrinsic excitations is designed. Corresponding policies and constraints are all provided for successful take-off, excellent flight attitude and stable standing after landing. In order to avoid low efficiency of random exploration, a curiosity module is introduced as extrinsic rewards to solve this problem. Additionally, the target-guided module encourages the robot explore closer and closer to desired jumping target. Simulation results indicate that the quadruped robot can realize completed forward jumping locomotion with good horizontal and vertical distances, as well as excellent motion attitudes.

The pathogenesis of cancer and cardiovascular diseases is subjected to spatiotemporal regulation by the tissue microenvironment. Multiplex visualization of the microenvironmental components, including immune cells, vasculature and tissue hypoxia, provides critical information underlying the disease progression and therapy resistance, which is often limited by imaging depth and resolution in large-volume tissues. To this end, light sheet fluorescence microscopy, following tissue clarification and immunostaining, may generate three-dimensional high-resolution images at a whole-organ level. Here we provide a detailed description of light sheet fluorescence microscopy imaging analysis of immune cell composition, vascularization, tissue perfusion and hypoxia in mouse normal brains and hearts, as well as brain tumors. We describe a procedure for visualizing tissue vascularization, perfusion and hypoxia with a transgenic vascular labeling system. We provide the procedures for tissue collection, tissue semi-clearing and immunostaining. We further describe standard methods for analyzing tissue immunity and vascularity. We anticipate that this method will facilitate the spatial illustration of structure and function of the tissue microenvironmental components in cancer and cardiovascular diseases. The procedure requires 1–2 weeks and can be performed by users with expertise in general molecular biology.

Deep-learning-based detection methods have been widely applied to industrial defect inspection. However, directly using vanilla detection methods fails to achieve satisfying performance due to the lack of identifiable features. In this article, a novel attention-based multiscale feature fusion module (AMFF) is proposed, aiming to enhance defect features and improve defect identification by leveraging attention mechanism in the feature fusion. AMFF includes self-enhanced attention module (SEAM) and cross-enhanced attention module (CEAM). SEAM is performed on a single feature map, which first adopts multiple dilation convolutions to enrich contextual information without compromising resolution and then utilizes the intralayer attention on the current feature map. CEAM takes both the current feature map and the adjacent feature map as input to perform cross-layer attention. The adjacent feature map is modulated with the guidance of the current feature map, which is then combined with the current feature map and the output of SEAM for final prediction. AMFF is utilized in current feature fusion networks, e.g., feature pyramid network (FPN) and path aggregation FPN (PAFPN), and is further integrated into prevalent detectors to guide them to pay more attention to defects rather than the background. Extensive experiments are conducted on two real industrial datasets released by Tianchi platform, i.e., fabric and aluminum defect datasets. For each dataset, 500 images are randomly selected for test and the rest for training. The proposed AMFF is demonstrated to significantly boost defect detection accuracy with acceptable computational cost, and the real-time performance could fully satisfy practical requirements.

This article presents a study on trajectory tracking control for an underactuated bionic underwater vehicle that is propelled by undulating fins. The vehicle's propulsion mechanism is inspired by marine organisms, and its working principle is briefly introduced. Accurate trajectory tracking control is challenging due to the coupling of fin motions and the lack of control degree of freedom. The multi-input and multi-output characteristics of the tracking control task, coupled with the existence of system constraints, impose high requirements for the controller. To solve these problems, a novel dynamics model-based predictive control strategy is proposed and the control output at the dynamics level is obtained. To stabilize the underactuated dynamics, a practical sidesway compensator is designed and integrated into the control framework. The convergence of the approach is guaranteed. Real-time precise tracking control is realized. Extensive simulations and real-world experiments are carried out and compared with other control methods. Reliability and superiority of the proposed approach is demonstrated with up to an 80% reduction in trajectory tracking sidesway errors.

The transformation of metastable ferrihydrite to stable hematite has been linked to magnetic enhancement in soils and sediments. However, the magnetic and structural characteristics associated with the transformation of arsenate (As(v))-coprecipitated ferrihydrite to hematite remain unclear. Herein, we have investigated the structural and magnetic characteristics of fresh and aged As(v)-ferrihydrite coprecipitates. Magnetic characterization indicates that the high As(v) loading weakens the ferrimagnetic susceptibility of fresh Fe–As(v) coprecipitates by forming Fe–AsO4complexes. In the low As(v)-loading system (Fe/As(v) ≥ 40), the increase in As(v) concentrations accelerates the growth of hematite particles with an expanded unit cell parameter c, and enhances magnetic susceptibility by changing both the surficial and internal lattice arrangements of hematite crystallites. In contrast, in the high As(v)-loading system (Fe/As(v) ≤ 20), the transformation of ferrihydrite is greatly retarded and the formation of poorly crystalline ferrimagnetic maghemite significantly enhances the magnetic susceptibility in the transformation products. Our findings provide new insights into the enhancement of magnetic properties of iron (oxyhydr)oxides in high arsenic soils and sediments, which are also essential for developing effective immobilization and long-term storage strategies for arsenic using iron (oxyhydr)oxides.

Disturbances caused by unknown dynamics and environmental factors render the automatic control of underwater vehicles extremely challenging. These effects are complex, time varying, and difficult to model accurately, leading to possible instability in the control process. This article focuses on the disturbance rejection problem of an underactuated bionic underwater vehicle (BUV). A novel control approach that combines the robustness of a model predictive controller with the modeling capabilities of a learning-based observer is proposed. More specifically, a practical Gaussian process (GP)-based disturbance observer is designed for online disturbance estimation. The interactive prediction results are integrated into a model predictive controller to obtain a refined dynamics model. The control framework optimally solves for the optimal control output to achieve control at the dynamics level. The proposed approach realizes disturbance rejection control while ensuring real-time application, constraint satisfaction, and recursive feasibility under complicated disturbances. The feasibility and efficiency of the proposed approach are verified through simulations and real-world experiments.

Thickness is a crucial parameter that significantly influences the optical, piezoelectric, and magnetoelectric properties of films. Reducing the film’s thickness nonuniformity at wafer scale is quite important for industrial manufacturing but difficult and challenging. Conventionally, the complementary metal-oxide-semiconductor transistor (CMOS)-compatible magnetron sputtering (MS) method employs planar targets, which often leads to an unsatisfactory thickness distribution, i.e., thick in the center and thin at the edges. To address this issue, a concave target is proposed based on the theoretical thickness distribution model and is experimentally manufactured to optimize the thickness nonuniformity of magnetron-sputtered films. Additionally, the influences of target size and target–substrate distance are also investigated. Our results demonstrate that using a concave target with a large radius is effective for improving the thickness uniformity. For instance, ultrauniform Mo films are deposited onto a 2-in. Si/SiO2wafer, exhibiting a notably low thickness nonuniformity of 0.38%. Our results provide theoretical guidance and the experimental prototype for the large-scale preparation of ultrauniform films. Moreover, these findings could be applied to other materials.

Light availability for phytoplankton in shallow lakes is closely related to non‐algal light attenuation (KdNALA, the fractional light absorption by non‐algal substances, such as suspended sediments); thus, significant changes in global wind speed in recent decades may have a profound effect on light availability and algal blooms in shallow lakes. Herein, the eutrophic shallow Lake Taihu was selected to investigate the long‐term dynamics of light availability and its effect on algal blooms. The results showed that KdNALAdecreased from approximately 3.5 to 2.5 m−1with decreasing wind speed from 2005 to 2021, indicating the significantly increased light availability for phytoplankton despite surface photosynthetically active radiation exhibiting limited variability during the study period. In addition, both experimental (i.e., in situ nutrient enrichment experiments) and statistical approaches (i.e., deviations of Trophic State Index subindices) indicated that phytoplankton growth was primarily light‐limited during the study period in Lake Taihu. Consequently, considerable increases in fraction of observed and maximal chlorophyll ayield at given nitrogen or phosphorus concentration were observed and were mostly related to decreasing KdNALA, which implied that decreasing KdNALAallowed phytoplankton utilize “unused nutrient‐capacity” until the additional algal‐turbidity induce further light limitation or nutrient limitation. As the effect of changes in global wind speed on KdNALAand algal growth received limited attention in the existing research, we revealed an underappreciated mechanism by which global changes in wind speed significantly affects algal biomass by influencing light availability, which may have profound effects on future algal bloom mitigation efforts in shallow lakes.

Urban sewage sludge treatment is important for sustainable utilisation and virtuous cycle of freshwater resources. However, with the improvement of sewage discharge standards, ensuring stable operation of sewage sludge treatment plants is becoming an urgent problem to be solved in the sewage treatment industry. This paper proposes a FNN control framework based on different working conditions to optimise the whole process of municipal sewage sludge treatment and discharge. The framework first divides the working conditions according to the weather, forming a separate feature and an input vector together with the typical indicators of other sewage treatment plants. Then the FNN is used to complete the control and optimisation of various indicators, achieving the dual objectives of reducing energy consumption and optimising water quality. Finally, the model is tested for the tracking index of sewage flow. The results demonstrate that the FNN control method used has significantly lower MAE than the single method in the two indexes of energy consumption and water quality evaluation. This provides new ideas for the optimisation of urban sewage sludge treatment process in the future. Overall, the paper effectively highlights the importance of urban sewage sludge treatment and presents a well-designed FNN control framework for optimising the treatment process. Additionally, the paper could benefit from further elaboration on the significance of the results obtained, and suggestions for future research in this area.

Moving object segmentation (MOS), aiming at segmenting moving objects from video frames, is an important and challenging task in computer vision and with various applications. With the development of deep learning (DL), MOS has also entered the era of deep models toward spatiotemporal feature learning. This paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three years. Specifically, we present a more up-to-date categorization based on model characteristics, then compare and discuss each category from feature learning (FL), and model training and evaluation perspectives. For FL, the methods reviewed are divided into three types: spatial FL, temporal FL, and spatiotemporal FL, then analyzed from input and model architectures aspects, three input types, and four typical preprocessing subnetworks are summarized. In terms of training, we discuss ideas for enhancing model transferability. In terms of evaluation, based on a previous categorization of scene dependent evaluation and scene independent evaluation, and combined with whether used videos are recorded with static or moving cameras, we further provide four subdivided evaluation setups and analyze that of reviewed methods. We also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of technology. Finally, based on the above comparisons and discussions, we present research prospects and future directions.

China is actively upgrading its industrial structure through industries transferring between developed and undeveloped areas; however, the overall level of the national value chain is still not high, and the asymmetric competition pattern between the upstream and the downstream has not been broken. Therefore, this paper establishes a competitive equilibrium model for the production of manufacturing enterprises, with factor price distortion, under the condition of constant returns to scale. The authors derive the relative distortion coefficients of each factor price, calculate the misallocation indices of capital and labor, and construct an industry resource misallocation measure. Furthermore, this paper applies the regional value-added decomposition model to calculate the national value chain index and matches the market index of the China Market Index Database with the Chinese Industrial Enterprises Database and the Inter-Regional Input-Output Tables through quantitative analysis. From the perspective of the national value chain, the authors study the improvement effect and mechanism of the business environment on the resource allocation in industry. The study shows that industry resource allocation will be improved by 17.89% if the business environment level is improved by one standard deviation. This effect is most prevalent in the eastern and central regions, not so much in the west; the effect of downstream industries in the national value chain is higher than that of upstream industries; the improvement effect on capital allocation is higher in downstream industries than in the upstream industries; and the improvement effect on labor misallocation is basically the same in both the upstream and the downstream. Compared with labor intensive industries, capital intensive industries are more influenced by the national value chain, while the effect of upstream industries is weaker. At the same time, it is well documented that participation in the global value chain can improve the efficiency of regional resource allocation, and the construction of high-tech zones can improve resource allocation for both upstream and downstream industries. Based on the results of study, the authors propose suggestions for optimizing business environments, suiting the national value chain construction, and improving resource allocation in the future.

As a kind of important strategic resource, molybdenum (Mo) and its alloys are widely applied in solar cells and 5G radio frequency filters. In order to improve the yield of industrial production, it is important to reduce the nonuniformity of thin-film thickness. We prepared Mo films on CMOS compatible Si/SiO2wafers through a magnetron sputtering method and systematically investigated the effect of tuning of several process parameters on film thickness nonuniformity and other structural properties, including crystallite size (D), microstrain (ε), and dislocation density (δ). At optimized sputtering power (80 W), chamber pressure (0.4 Pa), argon flow rate (90 sccm), substrate temperature (350 °C), and target–substrate distance (9 cm), the deposited 200 nm-thick Mo films show a unified <110> preferred orientation, and the film thickness nonuniformity reaches an excellent value of 0.46%, with a surface roughness of 0.83 nm and the full width at half maximum (FWHM) of X-ray diffraction (XRD) (110) rocking curve as low as 0.47°. Our results provide efficient reference for the preparation of uniform metal films, which have potential for improving the quality and yield in filter device manufacturing.

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0–5.3), P< 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7–6.4), P< 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83–7.60), P= 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.

Sound source localization (SSL) and approaching are essential capabilities for robots with auditory sensing. However, most existing methods for SSL only provide the direction of arrival (DoA) of the source, without the source distance, and cannot cope with time-varying number of sound sources. In this article, a novel framework that integrates multi source state estimation and source approaching control is proposed to address these issues. First, auditory probability hypothesis density (A-PHD) method is proposed, which can estimate both the source direction and distance by leveraging the robot motion information along with DoA estimation. A-PHD designs a new state update and merge strategy based on the characteristics of auditory perception, enabling more accurate source number estimation and real-time performance improvement. Second, a new source approaching control method aiming at both improving SSL accuracy and approaching the source is proposed, which utilizes the entropy to quantify the uncertainty of SSL. This method establishes the explicit relationship between the entropy and the robot motion, achieving efficient and smooth source approaching while improving SSL accuracy. Third, based on the methods proposed above, an organic framework consisting of source localization and approaching is formed. In this framework, the results of A-PHD serve as input of the control method, and in turn, the control method utilizes certain robot motions to improve the accuracy of A-PHD and obtains more precise source locations as input. In this process, more accurate SSL and more efficient source approaching are achieved. A series of experiments such as multisource state estimation with various DoA errors, robot motions, missing and false measurements, and source approaching are conducted on a mobile robot with a six-microphone array, verifying the effectiveness of our methods.

Caproic acid is the precursor material of ethyl hexanoate, a representative flavor substance in strong flavor baijiu (SFB). Increasing the content of caproic acid in SFB helps to improve its quality. In the present study, caproic acid-producing bacteria from the pit mud of an SFB ecosystem were isolated, purified, and characterized. Strain BF-1 with the highest caproic acid yield (0.88 g/l) was selected. The morphological and molecular identification analysis showed that strain BF-1 was Enterococcus casseliflavus. The genome of E. casseliflavusBF-1 was sequenced and was found to be 2,968,377 bp in length with 3,270 open reading frames (ORFs). The caproic acid biosynthesis pathway in E. casseliflavusBF-1 was predicted based on the KAAS annotation. The virulence factors in the genome of strain BF-1 were annotated, which showed that E. casseliflavusBF-1 is safe at the genetic level. After adding essential nutrients based on the KAAS annotation, the optimum medium conditions for acid production by strain BF-1 were obtained by performing orthogonal experiments. The caproic acid yield of strain BF-1 reached 3.03 g/l, which was 3.44-fold higher than the initial yield. The optimized fer-

In this paper, the bubble-point pressures and pseudo bubble-point pressures of various heavy crude oil−solvent systems were measured and studied by conducting the constant-composition-expansion (CCE) tests, during which the test pressure was depleted in a stepwise manner. A total of fourteen CCE tests were performed for five heavy crude oil−CO2systems, four heavy crude oil−CH4systems, and five heavy crude oil−C3H8systems, respectively. All the CCE tests were conducted by using a pressure−volume−temperature (PVT) system. It was found that for most heavy crude oil−solvent systems with relatively low solvent concentrations, the measured PVT cell pressure vs. molar volume (Pcell−νmix) data in the CCE tests had three distinct regions, which were one-liquid phase region (Regions I), foamy-oil region (Region II) and two-phase region (Region III). Accordingly, the PVT cell pressure at the intersection point of Regions I and II was referred to as the measured bubble-point pressure, whereas the PVT cell pressure at the intersection point of Regions II and III was termed as the measured pseudo bubble-point pressure. For some heavy crude oil−solvent systems with high solvent concentrations, however, the measured Pcell−νmixdata may have two regions only. In this special case, the PVT cell pressure at the intersection point of the two regions was considered as the measured bubble-point pressure and no pseudo bubble-point pressure could be obtained. It was also found that the heavy crude oil−CH4system had not only the highest bubble-point pressure and pseudo bubble-point pressure but also the largest difference between the bubble-point pressure and pseudo bubble-point pressure, in comparison with the heavy crude oil−CO2system and the heavy crude oil−C3H8system at the same solvent concentration. These facts indicate that CH4not only is the most volatile solvent but also can remain as dispersed gas bubbles to induce the most stable foamy oil in the largest pressure range after it is nucleated from a live heavy oil.

Recently, the development of efficient and non-noble photoelectrocatalysts with enhanced light absorption and high photoelectrochemical (PEC) performance has attracted increasing attention due to their potential in addressing the global fossil energy and environment crisis. In this work, we designed and prepared a topological insulator Bi2Te3film and non-noble plasmonic Cu nanoparticles onto one-dimensional (1D) TiO2nanorod (NR) array, forming TiO2/Bi2Te3/Cu photoanode. Benefiting from the synergistic effect of plasmonic Cu-induced hot electrons and Bi2Te3-supplied topological high-mobility electron channels, the PEC performance and charge separation of TiO2were enhanced. Accompanied by the improved light absorption, the optical band gap was narrowed from 3.02 eV for TiO2to 2.47 eV for TiO2/Bi2Te3/Cu. Moreover, the photocurrent density of pure TiO2was increased by about 3.05 times, from 0.77 mA/cm2at 1.23 V vs reversible hydrogen electrode (RHE) for TiO2to 2.33 mA/cm2for TiO2/Bi2Te3/Cu. Moreover, the recombination of photogenerated electron–hole pairs was also suppressed, and the carrier lifetime was prolonged from 24.6 ns for bare TiO2to 33.4 ns for TiO2/Bi2Te3/Cu. As a result, the TiO2/Bi2Te3/Cu photoanode showed good long-term cycling stability, with the H2generation rate from PEC water splitting reaching 20.3 μmol/cm2/h. Our results suggest that co-decorating topological insulators and plasmonic materials could be a promising strategy to improve the PEC performance of TiO2and may be applied in other photoelectrocatalysts.

Applying jumping locomotion into autonomous mobile robot is an effective way for improving abilities to overcome barriers and pass through complex terrains. In this paper, based on the designed structural framework for bio-inspired quadruped jumping robot, dynamic model is established by utilizing port-Hamiltonian with dissipation (pHd) method, and a passivity-based control strategy for the robot joints trajectory tracking is presented. First, morphology and biomimetics knowledges of frogs (a kind of animals with excellent jumping skill) motivate us to complete a bio-inspired jumping robot framework based on frog’s motion mechanism and body structure with torsional springs as energy storage device. Then, combining system passivity and dissipation, port-Hamiltonian method is utilized to build a dynamic model for expressing the relationship of energy and force in the designed robot. Jumping process analysis of different stages is also designed for ensuring the robot to complete taking-off and landing stages successfully. Next, with the definition of extending feasible robotic joint trajectory by La Salle invariant set principle, interconnection and damping assignment passivity-based control (IDA-PBC) method is exerted to obtain a trajectory controller for realizing smoothly and stably trajectory tracking in joint space. At last, simulation results show the reasonableness of the designed framework. By comparing our method with state-feedback and sliding mode control, effectiveness of the dynamic model and trajectory controller is also verified.

ABSTRACTArecoline, the most abundant alkaloid of the areca nut, induces toxicity to neurons. Hydrogen sulfide (H2S) is an endogenous gas with neuroprotective effects. We recently found that arecoline reduced endogenous H2S content in PC12 cells. In addition, exogenously administration of H2S alleviated the neurotoxicity of arecoline on PC12 cells. Increasing evidence has demonstrated the neuroprotective role of improvement of autophagic flux. Therefore, the aim of the present work is to explore whether improvement of autophagic flux mediates the protection of H2S against arecoline-caused neurotoxicity. Transmission electron microscope (TEM) for observation of ultrastructural morphology. Western blotting was used to detect protein expression of the related markers. Functional analysis contained LDH release assay, Hoechst 33,258 nuclear staining and flow cytometry were used to detect cytotoxicity and apoptosis. In the present work, we found that arecoline disrupted autophagy flux in PC12 cells as evidenced by accumulation of autophagic vacuoles, increase in LC3II/LC3I, and upregulation of p62 expression in PC12 cells. Notably, we found that sodium hydrosulfide (NaHS), the donor of H2S improved arecoline-blocked autophagy flux in PC12 cells. Furthermore, we found that blocking autophagic flux by chloroquine (CQ), the inhibitor of autophagy flux, antagonized the inhibitory role of NaHS in arecoline-induced cytotoxicity apoptosis and endoplasmic reticulum (ER) stress. In conclusion, H2S improves arecoline-caused disruption of autophagic flux to exert its protection against the neurotoxicity of arecoline.

Phase change buoyancy control systems (PBCS) driven by phase change materials have unique advantages over conventional hydraulically actuated buoyancy control systems, especially in their high adaptability for deep-sea exploration and seamless interaction with humans. Studying the heat transfer process and working mechanism of PBCS is helpful to research the precise control mode of the phase change buoyancy regulation system. In this study, we chose paraffin wax as the phase change material for PBCS. A buoyancy generator for phase change buoyancy regulation is designed and manufactured. By studying the relation between its expansion volume and heating time and heat input, the mathematical model of the volume change of PBCS is established. We obtain the calculation results of the PBCS working process by using the computer-aided software MATLAB. Experimental results show that the theoretical volume expansion velocity and volume expansion rate of paraffin are almost consistent with the experimental results. The mathematical calculation model is available and has a significant reference value for further research on the theoretical basis of the diving depth control of PBCS.

We investigate the phase transition from macroscopic oscillatory state to stable homogeneous steady state in a heterogeneous network of globally coupled Stuart–Landau limit cycle oscillators in the presence of the inertial effect. The phase transition, known as aging transition, onsets above a critical fraction of inactive constituents in the mixed population of active and inactive units. We show that even a feeble increase in the inertial strength increases the critical fraction of inactive units significantly for the onset of the phase transition to the macroscopic steady state thereby resulting in a more robust network, in general. In contrast, a large coupling strength, in the case of a homogeneous network, facilitates the manifestation of the phase transition even for a small fraction of inactive oscillators leading to a more fragile network. Nevertheless, a large coupling strength, in the case of a heterogeneous network, increases the resilience of the network by facilitating the phase transition at a large fraction of inactive oscillators. Furthermore, a larger standard deviation of the natural frequencies always leads to a more fragile network. We derive the macroscopic evolution equations for the order parameters and the stability curve using the first-order moment expansion around the mean-field. In addition, we also deduce the critical fraction of inactive units and the critical inertial strength analytically that matches with the simulation results. Interestingly, we find that the critical inertial strength is reciprocally related to the square of the mean frequency of the network. • Even a small inertial strength increases the robustness of the network. • Increase in the coupling strength increases robustness of the heterogeneous network. • Order parameters and stability curve are derived using first-order moment expansion. • Critical fraction of inactive units and the critical inertial strength are deduced. • Critical inertial strength is reciprocally related to square of the mean frequency. [ABSTRACT FROM AUTHOR]

Ultrahigh dose-rate (FLASH) proton therapy is of great interest due to potential reduced normal tissue toxicities without compromising tumor-killing effect compared to current clinical proton practices. However, the ionization chamber response to proton beams under ultrahigh dose rates (>40 Gy/s) has not been thoroughly investigated. In this study, four different ion chambers (PTW 34045 Advanced Markus, PPC-40, CC-04, and CC-13 from IBA Dosimetry) were irradiated with 230 MeV proton beams at 1.5, 63.7, and 127.6 Gy/s dose rates. Theoretical values of ion recombination correction factor ( ${k}_{s}$ ) were calculated from saturation curves using Niatel’s model. The theoretical ${k}_{s}$ values were compared to the values using the two-voltage (2V) method from standard dosimetry protocols and the three-voltage linear (3VL) method proposed by Rossomme et al. Both parallel plate chambers and CC-04 demonstrated adequate ion collection efficiency at the highest dose rate. For these three chambers, there is no statistically significant difference between theoretical ${k}_{s}$ values and those calculated with 2V and 3VL methods. However, significant ion recombination correction was found in CC-13 ( ${k}_{s}>1.50$ ) when dose rate reached 63.7 Gy/s. The assumption of insignificant initial recombination in standard dosimetry protocols also underestimated the ion recombination effect in this scenario.

Porous metal architectures are widely adopted as three-dimensional conducting scaffolds for constructing Li metal composite anodes, whereas their macropores hinder their practical application due to limited surface area and large pore size of few hundred micrometers. In this work, a network of LixCu solid solution alloy nanowires is in situformed viainfiltrating molten Li-Cu alloy into Ni foam and subsequent cooling treatment, whereby a three-component composite anode consisting of Li metal, LixCu alloy, and Ni foam is fabricated. The LixCu nanowires nested as secondary frame split the macropores into micropores, enlarging the active surface area and inducing uniform Li deposition significantly. The lithiophilicity of the alloy wires and the shrunken void size built by the hierarchical architecture can further tune the nucleation and growth behavior of Li. The multiscale synergetic effect between the primary and secondary scaffold guarantees the composite anode sheet with extraordinarily long-term cycling stability even under high current rates.

The digital camouflage spraying of special vehicles carried out by robots can greatly improve the spraying efficiency, spraying quality, and rapid adaptability to personalized patterns. The selection of spray tool and the accuracy of the adopted mathematical spray tool model has a great impact on the effectiveness of spray path planning and spraying quality. Since traditional conical spray tool models are not suitable for spraying rectangular digital camouflage, according to the characteristics of digital camouflage, the coating thickness cumulative distribution model of strip nozzle spray tool for 2D plane spraying and 3D surface spraying is derived, and its validity is verified by simulation. Based on the accumulation velocity model of the coating thickness (AVCT) on the curved surface and aiming at spraying path planning within the same surface and different surfaces, a path parameter optimization method based on coating uniformity evaluation of adjacent path overlapping area is proposed. Combined with the vehicle surface model, parameters such as path interval, spray tool angle and spray tool motion velocity can be calculated in real-time to ensure uniform coating. Based on the known local three-dimensional model of vehicle surface and the comprehensive spraying simulation, the validity of the purposed models: the coating thickness on the adjacent path area (CTAPA), the coating thickness on the intersection of two surfaces (CTITS), the coating thickness on the intersection of a plane and a surface (CTIPS), and the optimization method of path parameters are verified. The results show that compared with the traditional spray tool, the strip nozzle can better ensure the uniformity of the coating thickness of digital camouflage spray. Finally, according to a practical spraying experiment, the results prove that the proposed models not only are effective but also meet the practical industrial requirements and are of great practical value.

We report the final overall survival (OS) analyses of atezolizumab-carboplatin-paclitaxel (ACP [experimental arm]) and OS data with approximately 39.8 months of median follow-up with atezolizumab-bevacizumab-carboplatin-paclitaxel (ABCP) versus bevacizumab-carboplatin-paclitaxel (BCP) in chemotherapy-naive patients with metastatic nonsquamous NSCLC in the phase 3 IMpower150 study (NCT02366143).

Mobile edge computing is trending nowadays for its computation efficiency and privacy. The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs) in the internet. This paper predicts the overall sales volume of the enterprise through the classic ARIMA model, and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts, so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers, thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning. In general, machine learning relies on powerful computation and storage resources, while mobile edge computing typically provides limited computation resources locally. Therefore, it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

Development of a positron emission tomography (PET) system capable of in-situ imaging requires a design that can accommodate the proton treatment beam nozzle. Among the several PET instrumentation approaches developed thus far, the dual-panel PET scanner is often used as it is simpler to develop and integrate within the proton therapy gantry. Partial-angle coverage of these systems can however lead to limited-angle artifacts in the reconstructed PET image. We have previously demonstrated via simulations that time-of-flight (TOF) reconstruction reduces the artifacts accompanying limited-angle data, and permits proton range measurement with 1–2 mm accuracy and precision. In this work, we show measured results from a small proof-of-concept dual-panel PET system that uses TOF information to reconstruct PET data acquired after proton irradiation. The PET scanner comprises of two detector modules, each composed of an array of $4\times 4\times 30$ mm3 lanthanum bromide scintillator. Measurements are performed with an oxygen-rich gel-water, an adipose tissue equivalent material, and in vitro tissue phantoms. For each phantom measurement, a 2-Gy dose was deposited using 54–100 MeV proton beams. For each phantom, a Monte Carlo simulation generating the expected distribution of PET isotope from the corresponding proton irradiation was also performed. The proton range was calculated by drawing multiple depth profiles over a central region encompassing the proton dose deposition. For each profile, the proton range was calculated using two techniques 1) 50% pick-off from the distal edge of the profile and 2) comparing the measured and Monte Carlo profile to minimize the absolute sum of differences over the entire profile. A 10-min PET acquisition acquired with minimal delay post proton irradiation is compared with a 10-min PET scan acquired after a 20-min delay. Measurements show that PET acquisition with minimal delay is necessary to collect 15O signal, and maximize 11C signal collection with a short PET acquisition. In comparison with the 50% pick-off technique, the shift technique is more robust and offers better precision in measuring the proton range for the different phantoms. Range measurements from PET images acquired with minimal delay, and the shift technique demonstrates the ability to achieve &lt;1.5 mm accuracy and precision in estimating proton range.

Lithium (Li) metal is a promising anode material for high energy density Li batteries due to its high specific capacity and low redox potential. However, its practical applications are hindered by issues such as Li dendrites, side reactions, and volumetric changes. Li-rich alloys have demonstrated potential in addressing these issues, as they can be easily synthesized and form an in situthree-dimensional scaffold embedded with metallic Li. This review comprehensively summarizes the properties of representative Li-rich alloys, including binary and multi-element alloys. These alloys consist of both metallic and non-metallic elements, some of which can form solid solutions with Li, while others can form intermetallic compounds. The advantages and disadvantages of these alloys are compared and analyzed. Solid solution alloys are more stable than intermetallic compounds because there is no phase transformation within a certain range during the process of lithiation and delithiation. Li-rich alloys, such as Li–Mg, Li–Sn, and Li–Zn, exhibit promising merits, including high specific capacity, stable scaffold, high ionic conductivity, and low cost. This investigation provides a comprehensive perspective for the development of Li-rich alloy anodes towards practical application.

Investigating the mass transfer of toluene methylation with methanol will contribute to converting more methanol feedstock into the para-methyl group. The reaction system was considered as three competing reactions: toluene methylation and isomerization of para-xylene and methanol to light hydrocarbons. When the thiele modulus analysis method was employed, the degree of utilization of each reaction was calculated over varied ZSM-5 zeolite catalysts. It was found that improvement of mass transfer leads to a more significant increase in the degree of utilization of the methylation reaction. And to further reduce the degree of isomerization, modification of the micropore mouth and coverage of the external acid sites by MgO addition etc. is essential, which can lead to higher para-selectivity, with values from 34.9% to 86.2%. Constructing hierarchical pores in the ZSM-5 zeolite catalyst or reducing crystal size is of significance to converting methanol feedstock into the methyl group of xylene. To obtain a zeolite catalyst with higher methylation efficiency, mass transfer must be preimproved before surface modification.

Nongxiangxing baijiu (NXXB), a Chinese distilled spirit beverage, enjoys widespread popularity, because of its unique flavor profile, with short-chain fatty acids (SCFAs) and their ester derivatives making a major contribution to its distinctive flavor. SCFA-producing microorganisms (SPMs) determine the concentrations of these compounds, directly influencing the quality of NXXB. This article not only reviews the basic properties of SCFAs and their impact on the flavor of NXXB, but also comprehensively summarizes the research on SPMs in the NXXB fermentation ecosystem, including their distribution, SCFA biosynthetic pathways, regulation and optimization of SCFA production, and the factors affecting SCFA production. In addition, the paper discusses current progress in systems biology research and potential applications of SPMs, offering new insights for future research and applications of SPMs in the NXXB industry and other fermentation industries.

Establishing robust correspondences between two images is important for computer version tasks. However, in the real scene incorrect correspondences are inevitable no matter what kind of correspondence matching algorithms are adopted due to some complex factors, such as illumination, occlusion, and so on. To reduce the number of incorrect correspondences, an algorithm with the same object and same position constraints (SOSPC), is proposed to remove wrong correspondences from the given putative correspondences in this paper. The algorithm is based on the fact that in the given image pairs correct correspondences locate at the same position on the same objects. To select the correspondences on the same objects, an object matching method based on the correspondences selected by GMS is proposed. To select the correspondences on the correct positions, an iterative fundamental matrix estimation method based on clustering is presented. The experimental results have validated the effectiveness of the same object and the same position constraints, and the method achieves the state-of-art performance on five datasets.

Developing high-performance, energy-efficient, and cost-saving metal oxide-based gas sensors for NO2detection at room temperature is of immense importance. In this work, nonstoichiometric NiO-Nivwas prepared by a low-cost chemical coprecipitation method and then deposited as a sensing film for NO2detection at RT without further postannealing. The NiO-Nivsensor showed a maximal response value of 12.42 toward 20 ppm of NO2gas, 7.8 times higher than that of the pure NiO. Moreover, it exhibited quite high selectivity and stability to NO2gas, with its properties almost maintained even after 1 month. This exceptional sensing performance might be attributed to the unique surface morphology and abundant nickel vacancies that induced high activity in NiO-Niv. To investigate the sensing mechanism, the adsorption energy and charge density difference were calculated by first-principles calculations based on the density functional theory. It demonstrated that NiO with nickel vacancy can adsorb NO2more easily and that there was more charge transfer between NiO-Nivand NO2molecules compared to that of pure NiO. Furthermore, a potential gas-sensing mechanism involving nickel vacancies was proposed to elucidate the origin of the enhanced sensing property. The improved strategy can be broadly applied to p-type semiconductor-based gas sensors.

This article investigates an output feedback based proportional-derivative (PD)-type iterative learning fault-tolerant control approach for a category of uncertain discrete systems subject to actuator faults. To develop the iterative learning control scheme based on the output measurement, the fault system of repetitive nature is equivalently converted into a discrete linear repetitive process. By applying a Lyapunov function, this article establishes new adequate criteria for to ensure the asymptotic stability of the implemented fault-tolerant controller through linear matrix inequality constraints. The precise allocation of the controller gain matrices is also provided. Finally, simulation results of an injection molding process demonstrate the effectiveness and viability of the proposed approach.

A number of laboratory tests and some field-pilot tests have shown that CO2-based cyclic solvent injection (CO2-CSI) has a great commercial potential to enhance heavy oil recovery after the primary production. CO2-CSI timing post the primary production is an important and practical operating consideration in the oilfield applications. When to start CO2-CSI may considerably affect the primary production and CO2-CSI production as well as the total oil recovery factor (RF).

Using quantitative methods to distinguish tidal- and fluvial-dominated sedimentary systems is currently a challenge in sedimentological research. This paper establishes and verifies a new quantitative method through the degree of serration of gamma-ray logging curves. This method depends on the fundamentals of core-based and gamma-ray logging pattern interpretation of the depositional environment, using one-dimensional continuous wavelet transform to denoise the initial curve to enhance the effects of tides and rivers processes, and then expands to an analysis of the degree of serration in noise-reduced gamma-ray curves. The results may then be used to constrain a single best-boundary value to distinguish a tidal- or fluvial-dominated area, avoiding subjectivity and uncertainty. A case study of the Pinghu Formation showed that the degree of serration (defined here as DeltaGr) of the fluvial-dominated sediments was less than 10 in the depth dimension and that the average degree of serration (defined here as the average square of DeltaGr) was less than 7 in the layer dimension. Values that deviate from these imply a close relation to tide-dominated sedimentary systems, which provides a quantitative basis for the interpretation of sedimentary environments in both the depth and layer dimensions in the Pingbei Slope. Seismic attribute-based sedimentary analysis, relative sea-level changes, and the basin architecture of the Pingbei Slope clarify the reason for these changes and verify that this method is reliable. However, owing to the limitations of logging and core samples, the criteria remain an approximation of the actual situation. This study may, however, enable the discrimination of other similar sedimentary systems dominated by tidal and fluvial processes and therefore provide a further constraint on the reconstruction of the filling of basins.

Jiuqu, a saccharification and fermentation agent used in the production of baijiu, is rich in enzymes, microorganisms, and organisms that have essential effects on the flavor quality of baijiu. However, traditional Jiuqu has some deficiencies in quality or may require enhancement of specific functions at times. To address this, fortified Jiuqu has been developed and applied to enhance performance and improve baijiu quality. This review focused on the functional microorganisms and strengthening effects of fortified Jiuqu. Moreover, this review also discussed the limitations of current Jiuqu preparation and fortification and explored the roles of the core functional microbes and the synthetic microflora for fortified Jiuqu, which could improve the cleanliness, controllability, and food safety of Jiuqu production.

Platinum group metal (PGM)-free single atom catalysts (SACs) are highly desired to accelerate the sluggish oxygen reduction reaction (ORR) in acidic media, which has a critical role in renewable energy technologies. Herein, we report the direct transformation of commercial graphite into iron (Fe) single atom anchored graphitic flake via a combination of mechanochemical and thermochemical synthesis. By ball milling graphite with zirconium dioxide balls and capsule in the presence of dry ice, the graphite was mechanochemically functionalized and exfoliated into carboxylated graphitic flake (CGF). Subsequent heat-treatment of CGF with nitrogen containing additives formed nitrogenated graphitic flake (NGF) incorporated with Fe single atoms (Fe1NGF). Importantly, the existing Fe impurity in the pristine graphite was converted into value-added product without adding Fe sources. With an ultralow Fe content (0.4 wt%), the as-prepared Fe1NGF exhibited an acidic ORR performance comparable to Pt/C, including ultrahigh mass activity, surpassing the other reported outstanding Fe-based SACs.

The ability to not only control but also maintain the well-defined size of nanoclusters is key to a scientific understanding as well as their practical application. Here, we report a synthetic protocol to prepare and stabilize nanoclusters of different metals and even metal salts. The approach builds on a Pickering stabilization effect inside a microemulsion system. We prove that the emulsion interface plays a critical role in the formation of nanoclusters, which are encapsulated in situ into a silica matrix. The resulting nanocapsule is characterized by a central cavity and a porous shell composed of a matrix of both silica and nanoclusters. This structure endows the nanoclusters simultaneously with high thermal stability, good biocompatibility, and excellent photostability, making them well suited for fundamental studies and practical applications ranging from materials chemistry, catalysis, and optics to bioimaging.

Hyperlipidemia has been demonstrated to evoke Alzheimer disease (AD) pathologies such as Amyloid-β (Aβ) deposition and synaptogenesis dysfunction in the hippocampus. Curcumin gives protection against anti-amyloid properties and synaptogenesis dysfunction. Curcumin-Nicotinate (CurTn), a new type of curcumin derivative, ameliorates cognitive impairment by rescuing autophagic flux in the CA1 hippocampus of diabetic rats. However, whether Curtn possesses an antagonistic effect on AD-related pathologies in the hippocampus induced by hyperlipidemia remains ill-defined. The present study aims to investigate whether CurTn alleviates synaptogenesis dysfunction by promoting the activation of brain-derived neurotrophic factor (BDNF)/tyrosine kinase receptor B (TrkB)/cAMP-response element binding protein (CREB) signaling and whether the underlying fundamental mechanism involves the elimination of Aβ deposition due to Idol/low-density lipoprotein receptor (LDLR) signaling in the hippocampus of high-fat diet (HFD)-induced hyperlipidemia rats. The results demonstrated that CurTn not only improved synaptogenesis dysfunction in the hippocampus of HFD rats, as evidenced by the increases in the expressions of synapse-related proteins postsynaptic density protein 95 (PSD-95), synapsin-1, and Glutamate receptor 1 (GluR1), but also activated BDNF/TrkB/CREB signaling, as evidenced by the elevation of the expressions of BDNF, pTrkB, and CREB. Moreover, CurTn modulated the Idol/LDLR pathway in the hippocampus of HFD rats, as evidenced by the decreased expression of Idol and the increased expression of LDLR. Furthermore, CurTn eliminated the deposition of Aβ, as evidenced by the reduction in the content of Aβ40 and Aβ42. These results reveal that CurTn may attenuate synaptogenesis dysfunction by activating BDNF/TrkB/CREB signaling, as the possible result of the modulation of Idol/LDLR signaling to eliminate Aβ deposition in the hippocampus of HFD rats.

Safety of drug use during pregnancy attracts attentions from clinicians, pregnant woman, and even the total society. However, the studies about medication use during pregnancy and the followed birth defects (BDs) are rare in Chinese. To study condition about medication use during pregnancy and the followed BDs in Hunan province of China, here a cross-sectional study was carried out. All women using medication during pregnancy and delivering fetuses with BDs in Hunan province, China, during 2016 to 2019 were employed in this study. The descriptive analysis was carried out with Excel 2010, and the data analyses were performed by using Chi-Squared test in SPSS 16.0. After filtering, a total of 752 cases were included. In these fetuses, the males are more than females (P < .05). The severe BDs, leading to death or uncorrectable lifelong deformity, were observed for 346 times, and the other (minor) BDs were observed for 593 times. The most used drugs, categorized into pregnancy C, D, or X degrees by food and drug administration (FDA) or pharmaceutical manufacturers, mainly included anti-hyperthyroidism drugs, anti-epilepsy drugs, preventing miscarriage drugs, etc. This population-based data highlight the potential high risks for BDs from the aspect of drug use during pregnancy in Hunan province of China, and drugs with more safety, less kinds, and lower doses should be the better choice for pregnant women.

Thorough investigations of the environmental fate and risks are necessary for the safe application of engineered nanomaterials. Nevertheless, the current understanding of potential transformations of MoS2(an intensively studied two-dimensional nanosheet) upon interactions with ubiquitous environmentally relevant thiols (ERTs) in water is limited. This study revealed that two ERTs, l-cysteine and mercaptoacetic acid, could modify MoS2by covalently grafting thiol groups on S atoms of 1T phases, improving the colloidal persistence and chemical stability of MoS2. Compared with the pristine form, MoS2–thiols with higher dispersity exhibited significantly mitigated envelopment and ultrastructural damage to microalgae. MoS2-triggered growth inhibition, upregulation of reactive oxygen species, photosynthetic injury, and metabolic perturbation in algae were remarkably attenuated by ERTs. The diminished capability for MoS2to generate reactive intermediates and glutathione oxidation driven by ERTs caused the weakness of oxidative stress and negative effects. Additionally, molecular dynamics simulations demonstrated that ERTs altered the extent of the influence of MoS2on the secondary structures and functions of adsorbed intracellular proteins, which also contributed to the lower phytotoxicity of MoS2. Our findings provide evidence for the crucial role of specific organic ligands in the risk of MoS2in aquatic environments.